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In this study, we explore the challenge of efficiently representing scenes with a constrained number of Gaussians.
Pauly, M., Gross, M., Kobbelt, L.P.: Efficient Simplification of Point-Sampled Surfaces. In: VIS. pp. 163–170. IEEE (2002)
2002
Earlier work this paper cites.
Zwicker, M., Pfister, H., Van Baar, J., Gross, M.: EWA Splatting. TVCG 8
2002
Earlier work this paper cites.
Moenning, C., Dodgson, N.A.: Intrinsic Point Cloud Simplification. Proc. 14th GrahiCon 14
2004
Earlier work this paper cites.
Katz, S., Tal, A.: Improving the Visual Comprehension of Point Sets. In: CVPR. pp. 121–128 (2013)
2013
Earlier work this paper cites.
De Queiroz, R.L., Chou, P.A.: Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform. IEEE TIP 25
2016
Earlier work this paper cites.
Schönberger, J.L., Zheng, E., Pollefeys, M., Frahm, J.M.: Pixelwise View Selection for Unstructured Multi-View Stereo. In: ECCV (2016)
2016
Earlier work this paper cites.
Chen, S., Tian, D., Feng, C., Vetro, A., Kovačević, J.: Fast Resampling of 3D Point Clouds via Graphs. IEEE Transactions on Signal Processing 66
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Knapitsch, A., Park, J., Zhou, Q.Y., Koltun, V.: Tanks and Temples: Benchmarking Large-Scale Scene Reconstruction. TOG 36
2017
Earlier work this paper cites.
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. In: CVPR. pp. 652–660 (2017)
2017
Earlier work this paper cites.
Hedman, P., Philip, J., Price, T., Frahm, J.M., Drettakis, G., Brostow, G.: Deep Blending for Free-Viewpoint Image-Based Rendering. SIGGRAPH Asia (2018)
2018
Earlier work this paper cites.
Dovrat, O., Lang, I., Avidan, S.: Learning to sample. In: CVPR. pp. 2760–2769 (2019)
2019
Earlier work this paper cites.
Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., Markham, A.: RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. In: CVPR. pp. 11108–11117 (2020)
2020
Earlier work this paper cites.
Lang, I., Manor, A., Avidan, S.: SampleNet: Differentiable Point Cloud Sampling. In: CVPR. pp. 7578–7588 (2020)
2020
Earlier work this paper cites.
Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. ECCV (2020)
2020
Earlier work this paper cites.
Barron, J.T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., Srinivasan, P.P.: Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields. ICCV (2021)
2021
Cited alongside, same era.
Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields. CVPR (2022)
2022
Cited alongside, same era.
Deng, K., Liu, A., Zhu, J.Y., Ramanan, D.: Depth-supervised NeRF: Fewer Views and Faster Training for Free. In: CVPR. pp. 12882–12891 (2022)
2022
Cited alongside, same era.
Lv, C., Lin, W., Zhao, B.: Intrinsic and Isotropic Resampling for 3D Point Clouds. PAMI 45
2022
Cited alongside, same era.
Müller, T., Evans, A., Schied, C., Keller, A.: Instant Neural Graphics Primitives with a Multiresolution Hash Encoding. SIGGRAPH (2022)
2022
Cited alongside, same era.
Li, L., Shen, Z., Wang, Z., Shen, L., Bo, L.: Compressing Volumetric Radiance Fields to 1 MB. In: CVPR. pp. 4222–4231 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Rho, D., Lee, B., Nam, S., Lee, J.C., Ko, J.H., Park, E.: Masked Wavelet Representation for Compact Neural Radiance Fields. In: CVPR. pp. 20680–20690 (2023)
2023
Later among the works it cites.
Wen, C., Yu, B., Tao, D.: Learnable Skeleton-Aware 3D Point Cloud Sampling. In: CVPR. pp. 17671–17681 (2023)
2023
Later among the works it cites.
Xie, X., Gherardi, R., Pan, Z., Huang, S.: HollowNeRF: Pruning Hashgrid-Based NeRFs with Trainable Collision Mitigation. In: ICCV. pp. 3480–3490 (2023)
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Sun, C., Sun, M., Chen, H.T.: Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction. CVPR (2022)
2022
Cited alongside, same era.
Xu, Q., Xu, Z., Philip, J., Bi, S., Shu, Z., Sunkavalli, K., Neumann, U.: Point-NeRF: Point-based Neural Radiance Fields. In: CVPR. pp. 5438–5448 (2022)
2022
Cited alongside, same era.
Yu, A., Fridovich-Keil, S., Tancik, M., Chen, Q., Recht, B., Kanazawa, A.: Plenoxels: Radiance Fields without Neural Networks. CVPR (2022)
2022
Cited alongside, same era.
Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields. ICCV (2023)
2023
Cited alongside, same era.
Deng, C.L., Tartaglione, E.: Compressing Explicit Voxel Grid Representations: fast NeRFs become also small. In: WACV. pp. 1236–1245 (2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Later among the works it cites.
Chen, G., Wang, W.: A Survey on 3D Gaussian Splatting. arXiv preprint arXiv:2401.03890 (2024)
2024
Closest in time.
2024
Closest in time.
Luiten, J., Kopanas, G., Leibe, B., Ramanan, D.: Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis. 3DV (2024)
2024
Closest in time.
2024
Closest in time.
Qian, S., Kirschstein, T., Schoneveld, L., Davoli, D., Giebenhain, S., Nießner, M.: GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians. CVPR (2024)
2024
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Sun, J.M., Wu, T., Gao, L.: Recent Advances in Implicit Representation-based 3D Shape Generation. Visual Intelligence 2
2024
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Tang, J., Ren, J., Zhou, H., Liu, Z., Zeng, G.: DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation. ICLR (2024)
2024
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2024
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Yu, Z., Chen, A., Huang, B., Sattler, T., Geiger, A.: Mip-Splatting: Alias-free 3D Gaussian Splatting. CVPR (2024)
2024
Closest in time.